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AI in mental health is a growing field that uses artificial intelligence to improve mental health care in several ways, including diagnosis, treatment, and support. Key areas of focus include:

1. Diagnosis and Screening

AI tools can analyze large amounts of data from patients' speech, behavior, and physiological responses to identify patterns associated with mental health conditions. For example, natural language processing (NLP) techniques are used to assess speech for signs of depression, anxiety, or other disorders, helping clinicians identify conditions earlier.

2. Personalized Treatment

AI can assist in creating personalized mental health treatment plans by analyzing patient data such as medical history, therapy outcomes, and genetic factors. This allows healthcare providers to offer more tailored treatment options, such as suggesting specific therapy types (e.g., cognitive-behavioral therapy or mindfulness) that align with the patient's unique needs.

3. Virtual Therapists and Chatbots

AI-based mental health chatbots provide 24/7 support for patients. These chatbots use cognitive-behavioral techniques and empathy to engage with users, helping them manage symptoms of anxiety, stress, or depression in real-time. While not a replacement for human therapists, they are valuable tools for providing immediate help.

4. Predicting Relapses and Crisis Management

AI systems are being developed to predict mental health relapses or crises by continuously monitoring patient data. Machine learning algorithms can identify patterns that suggest a patient is at risk of worsening mental health and alert clinicians or caregivers for timely intervention.

5. Data-Driven Research

AI can analyze massive datasets from clinical trials, patient records, or even social media to identify new mental health trends and treatments. This helps accelerate research in identifying novel therapeutic approaches or understanding how mental health disorders develop.

6. Reducing Stigma and Increasing Accessibility

AI tools have the potential to reduce the stigma associated with seeking mental health treatment by offering privacy and accessibility. These tools allow people to seek help without the fear of judgment and make mental health care more accessible, especially in areas with limited healthcare resources.

The use of AI in mental health is continually evolving, with advancements in machine learning and natural language processing offering more sophisticated and sensitive tools for diagnosing and treating mental health issues. However, ethical considerations, such as data privacy and the limitations of AI in providing empathetic care, are also critical topics in this domain.

 

What information could AI provide about mental health? Computational psychiatry has the potential to gain insight into any condition with a large enough dataset. Machine learning could identify which genes contribute to the development of autism or the factors that render adolescents vulnerable to binge-drinking such as brain size or parental divorce. These programs could reveal which systems are affected by dopamine in patients with Parkinson’s disease, or a person’s risk for depression based on factors such as sex and childhood trauma. Could AI help diagnose bipolar disorder? Artificial intelligence has the potential to leverage large datasets to improve diagnoses and reduce misdiagnoses. For example, depressive episodes in bipolar disorder and depression can be difficult to distinguish; many patients with bipolar are misdiagnosed with major depressive disorder. A machine learning algorithm that used self-reports and blood samples recently identified bipolar disorder patients in various scenarios, potentially providing a helpful supplement for clinicians in the future

 

Could AI help diagnose autism?

There are currently no medical tests to definitively diagnose autism, but a recent study demonstrated that a machine learning algorithm identified proteins that differed in boys with autism and that predicted the severity of the condition. As this technology continues to evolve, it could help diagnose autism based on biomarkers.

Could AI help diagnose schizophrenia?

A recent study found that a machine learning algorithm classified cases of schizophrenia based on brain images with 87 percent accuracy. The pattern-recognition skills and predictive abilities of AI could provide a valuable tool for clinicians diagnosing schizophrenia.

Can AI help treat mental illness?

It’s important to translate the fascinating discoveries of AI into applications that can really help people. This can involve pinning down predictive risk factors for psychiatric conditions: Are there specific brain regions that make people more likely to commit suicide? Or to become depressed? Are certain medications effective for some patients with schizophrenia but not others? Doctors can then assess patient risk and provide proactive, personalized mental healthcare.

Can AI accelerate drug discovery?

Artificial intelligence can analyze massive datasets for difficult-to-spot connections between drugs, diseases, and biological processes to identify potential treatments. For example, a machine learning framework recently predicted which of the 20,000 FDA-approved drugs had the greatest likelihood of helping to treat Alzheimer's disease.

The Ethics of Artificial Intelligence

 

The evolution of artificial intelligence has led to countless ethical questions. Will machine learning perpetuate bias and inequality? Will AI infringe on human privacy and freedoms? Will humans lose their jobs to robots? Will machines become more intelligent than humans?

People are right to question the nature of machines that can evolve on their own. By actively engaging with these concerns, hopefully humans can develop ethical systems of artificial intelligence moving forward.

Why does the technology sector pose unique ethical challenges?

People interact with technology on an unprecedented scale and in many different environments—at work, in the supermarket, in the car, at home. Technology deployers have some responsibility to keep people safe as AI poses ethical challenges. Whether it’s anticipating systemic bias, recognizing when technologies coerce decision-making, intercepting malicious actors who wish to weaponize platforms, or taking a stand on overzealous surveillance, creators and consumers need to make sure that technology serves the population well.

What are some underappreciated ethical concerns about AI?

One ethical concern about artificial intelligence is the potent yet subtle influence of technology on people’s choices and decision-making. Companies are able to use all of the information they store about people to their advantage—“nudging” people towards decisions that are predominantly in the company’s interests. Another concern may arise from the technologies that claim to be able to read and interpret human emotions. The idea of a product deceiving a child or vulnerable adult into believing it truly “understands them,” and thereby influencing them, is worrying.

How can people build ethical AI?

This goal requires a moral approach to building AI systems and a plan for making AI systems ethical themselves. For example, developers of self-driving cars should be considering their social consequences, including ensuring that the cars themselves are capable of making ethical decisions. Building ethical artificial intelligence involves addressing ethical questions (e.g. how to prevent mass unemployment) and ethical concerns (e.g. clarifying moral values) and then developing a plan that aims to satisfy human needs.

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Just as Steve Jobs saw the GUI as a world-changing inflection point, we're now at an even more profound moment with LLMs.

Key points

• LLMs struggle to fully grasp the deeply embedded trends and language patterns of internet culture.

• Rickrolling, a classic internet prank, reveals the limitations of machine intelligence in understanding.

• Machine intelligence mimics human communication but lacks true understanding, especially in humor.

• We don't yet fully understand the impact of training machine intelligence on vast datasets of online language.

Language models playing internet pranks

Some trends and language patterns are so deeply embedded in the language and culture of the internet that even the most advanced language models struggle to fully extract or contextualize them.

Humor, especially the internet’s unique and eccentric styles of jokes can help to explain the difference between human and machine intelligence. Apparently, large language models have a Rickrolling problem.

Rickrolling is an internet prank that's been an internet joke for decades. It uses hyperlinks to create a surprise connection. A link that appears to direct you to a meta-analytic research article on humor styles may actually redirect you to Rick Astley's 1987 hit "Never Gonna Give You Up."

Rickrolling and internet humor

This bait-and-switch is one of the most enduring internet pranks. It plays with the way technology (a hyperlink) is embedded in language. But it also is a subtle warning. Clicking a hyperlink on the internet may take you somewhere you didn’t expect. Rickrolling is a light and humorous way to remind people not to take everything online at face value.

There have been instances of large language models inadvertently Rickrolling users or chatbots pulling off a prank on unsuspecting clients of company chatbots.

This reveals much about human intelligence as well as the way language models work.

The culture of the internet is a vast repository of language, context, and subtlety. Simple jokes and cues require both an understanding of context and social situations. Human intelligence has evolved to quickly pick up subtleties from limited and ambiguous information.

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Social functions of humor

Jokes and pranks convey information on multiple levels and serve social functions. On the surface, they can showcase the personality of the individual telling the joke, whether they’re easy-going, friendly, or mischievous. But jokes can accomplish other objectives: signaling levels of familiarity, setting boundaries for conversation topics, and even sending covert messages about power dynamics.

A joke that demeans a person or group often reveals more about the joke-teller’s social intentions and perceived status. Whereas a self-deprecating joke can cut through social divisions and formalities, serving as a way to defuse tension or bring people closer. In both cases, humor is a social tool—either to reinforce or challenge social hierarchies.

Rickrolling is a perfect example of how an internet joke can operate on multiple levels. While it’s often just a light-hearted prank, it also carries a subtle warning: On the internet, you can’t always trust what you see. This reflects a broader truth and warning about the online world: What you encounter might not always be what you expect.

Humor reveals layers of language and culture

But it’s also revealing about how our language shapes the internet. The web is a vast network of language connected into networks of relationships and meaning. A prank like Rickrolling is used so often that it is deeply embedded in the language and code of the internet.

Large language models trained on vast datasets harvested from the web cannot simply ignore these elements of digital culture. Rickrolling is so ubiquitous that it shows up in the output of large language models that have failed to decode the context or social meaning of the joke.

Human vs. machine intelligence

Neil Lawrence, DeepMind Professor of Machine Learning at the University of Cambridge, explains the difference between human and machine intelligence in his book The Atomic Human. Lawrence deliberately uses the term ‘machine intelligence’, not 'artificial intelligence,’ to emphasize that machine intelligence is fundamentally different from how humans process information.

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Human intelligence is not just about processing data; it’s deeply embodied and evolved to handle ambiguity, make quick decisions with limited information, and rely on situational cues and social context. Machine intelligence can process vast amounts of data extraordinarily quickly to mimic human outputs. Yet it lacks true understanding, embodied knowledge, or the capacity to focus on specific information based on subtle situational cues. That means human intelligence is uniquely suited for environments and situations where nuance and context are key. Humor is the perfect example.

This is why a machine might replicate a Rickroll without any real understanding of the joke. It’s a combination of trust, deception, and unexpected results that is so attractive to human intelligence.

A caution embedded in the context

Rickrolling is a harmless prank that hints at a greater potential security risk. The fact that this has been incorporated into machine intelligence is extraordinarily funny, but it is also a warning.

The culture and the language of the internet are now deeply embedded in everything that is produced and created from it. All the human elements of trust, deception, unintended consequences, and uncertain relationships are wired into the connections of the network.

References

Lawrence, N., Montgomery, J., Schölkopf, B. (2023). Machine Learning for Science: Mathematics at the Interface of Data-driven and Mechanistic Modelling. Oberwolfach Reports(2), 1453–1484

Lawrence, N. D. (2024). The Atomic Human: Understanding ourselves in the age of AI. Allen Lane.

Marsh, M. (2019). American jokes, pranks and humor. In S. J. Bronner (Ed.). The Oxford Handbook of American Folklore and Folklife Studies. Oxford University Press.

MacRae, I. (2024) Web of value: Understanding blockchain and web3’s intersection of technology, psychology and business. Alexandria Books.

Silberling, A. (2024). This founder had to train its AI not to Rickroll people. TechCrunch.

The Psychologist (British Psychological Society):
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